Vanilla text-to-image diffusion models struggle with generating accurate
human images, commonly resulting in imperfect anatomies such as unnatural
postures or disproportionate limbs.Existing methods address this issue mostly
by fine-tuning the model with extra images or adding additional controls --
human-centric priors such as pose or depth maps -- during the image generation
phase. This paper explores the integration of these human-centric priors
directly into the model fine-tuning stage, essentially eliminating the need for
extra conditions at the inference stage. We realize this idea by proposing a
human-centric alignment loss to strengthen human-related information from the
textual prompts within the cross-attention maps. To ensure semantic detail
richness and human structural accuracy during fine-tuning, we introduce
scale-aware and step-wise constraints within the diffusion process, according
to an in-depth analysis of the cross-attention layer. Extensive experiments
show that our method largely improves over state-of-the-art text-to-image
models to synthesize high-quality human images based on user-written prompts.
Project page: https://hcplayercvpr2024.github.io.

该研究论文探索将人类中心先验直接整合到模型微调阶段，通过人类中心对齐损失强化文本提示中的人类相关信息，以及通过扩散过程中的比例感知和逐步约束确保语义详细性和人类结构准确性，从而提高了合成基于用户编写提示的高质量人类图像的方法。